In silico approaches to identify polyphenol compounds as α-glucosidase and α-amylase inhibitors against type-II diabetes

J Riyaphan, DC Pham, MK Leong, CF Weng - Biomolecules, 2021 - mdpi.com
Type-II diabetes mellitus (T2DM) results from a combination of genetic and lifestyle factors,
and the prevalence of T2DM is increasing worldwide. Clinically, both α-glucosidase and α …

[HTML][HTML] A narrative review: The pharmaceutical evolution of phenolic syringaldehyde

J Wu, YS Fu, K Lin, X Huang, Y Chen, D Lai… - Biomedicine & …, 2022 - Elsevier
To better understand the pharmacological characters of syringaldehyde (SA), which is a key-
odorant compound of whisky and brandy, this review article is the first to compile the …

[HTML][HTML] Artificial intelligence in antidiabetic drug discovery: The advances in QSAR and the prediction of α-glucosidase inhibitors

AI Odugbemi, C Nyirenda, A Christoffels… - Computational and …, 2024 - Elsevier
Artificial Intelligence is transforming drug discovery, particularly in the hit identification phase
of therapeutic compounds. One tool that has been instrumental in this transformation is …

Machine Learning Study of Metabolic Networks vs ChEMBL Data of Antibacterial Compounds

K Diéguez-Santana, GM Casanola-Martin… - Molecular …, 2022 - ACS Publications
Antibacterial drugs (AD) change the metabolic status of bacteria, contributing to bacterial
death. However, antibiotic resistance and the emergence of multidrug-resistant bacteria …

Chemical Profile and Biological Activities of Fungal Strains Isolated from Piper nigrum Roots: Experimental and Computational Approaches

ND Luyen, LM Huong, NTT Ha, NT Tra… - Chemistry & …, 2023 - Wiley Online Library
The current report describes the chemical investigation and biological activity of extracts
produced by three fungal strains Fusarium oxysporum, Penicillium simplicissimum, and …

Chemical feature-based machine learning model for predicting photophysical properties of BODIPY compounds: density functional theory and quantitative structure …

GM Casanola-Martin, J Wang, J Zhou… - Journal of Molecular …, 2025 - Springer
Methods In the present study, all the BODIPY models studied were fully optimized, and the
corresponding absorption spectrum was obtained at DFT/TDDFT//B3LYP/6-311G (d, p) …

A fuzzy system classification approach for QSAR modeling of α-amylase and α-Glucosidase Inhibitors

K Diéguez-Santana, A Puris… - … -Aided Drug Design, 2022 - ingentaconnect.com
Introduction: This report proposes the application of a new Machine Learning algorithm
called Fuzzy Unordered Rules Induction Algorithm (FURIA)-C in the classification of druglike …

Machine Learning Study of Metabolic Networks vs ChEMBL Data of Antibacterial Compounds

K Diéguez, G Casañola, R Torres, B Rasulev… - 2022 - digital.csic.es
Antibacterial drugs (AD) change the metabolic status of bacteria, contributing to bacterial
death. However, antibiotic resistance and the emergence of multidrug-resistant bacteria …

[PDF][PDF] MOL2NET, International Conference Series on Multidisciplinary Sciences

MM Nachimba-Mayanchi, K Diéguez-Santana - sciforum.net
MOL2NET, 2021, 7, ISSN: 2624-5078 2 https://mol2net-07. sciforum. net/bahia). The optimal
nonlinear model (Random Forest, R2= 0.983) was verified by internal (leave one cross …

[PDF][PDF] NANOBIOMAT-07: Nanotech. & Mat. Sci. Congress, Birmingham & Portsmouth, UK-Jackson & Fargo, USA, 2021.

The emergence of Multidrug-Resistant (MDR) strains promotes the improvement of
Antibacterial Drugs (AD). Some nanoparticles (NP) may be AD carriers, but some have …